The overall objective of this project is to dramatically improve the capability of fetal MRI for diagnosis, analysis, and prognosis of craniofacial developmental disorders. These disorders present in three quarters of all human birth defects and affect approximately one in every 500 live births. Craniofacial disorders may cause serious long-term problems that affect the quality of life and result in higher disease risk. Improved knowledge of early craniofacial development and developmental disorders helps for better prognosis and treatment, better management of pregnancy, improved neonatal care, and better long-term outcomes. Non-invasive medical imaging techniques are the main tools to acquire information about the fetus in-utero. Prenatal sonography is routinely performed in the second trimester of pregnancy, and is considered to be the primary diagnostic tool. Nevertheless the diagnostic accuracy of sonography for complex craniofacial diseases such as cleft lip and cleft palate, hemifacial microsomia, micrognathia, etc. is extremely low. On the other hand magnetic resonance imaging (MRI) has become an excellent complement to sonography for accurate diagnosis and analysis of such complex diseases. Nonetheless, fetal MRI is limited to two-dimensional acquisitions by small signal available from the small fetal organs, and by intermittent fetal motion that disrupts spatial encoding necessary for accurate three-dimensional analysis. Novel image processing technology has recently been developed for the reconstruction of high-resolution three-dimensional MRI of the fetal brain. This technology has led to significant improvements in fetal neuroimaging. However, this technology cannot be directly or simply adapted to fetal MRI of craniofacial structures; the non-rigid and local movement of soft tissue, fluid, and craniomaxillofacial bones and semi-bony structures pose significant challenges in volume reconstruction. The specific aim of this proposal is the development of novel models of soft tissue, fluid, and bone in craniofacial structures and local motion estimation based on these models. This will also be considered through the use of advanced image regularization techniques without explicit sub-voxel motion estimation. The specific aims in this proposal also involve the reconstruction of high-resolution fetal craniofacial MRI and their classification based on various types of disorders. The collected data will be shared with radiologists at Children's Hospital Boston as well as with the greater community of craniofacial experts on FaceBase consortium.